class_docstr: Match LSTM model.
  
  Examples:
      >>> model = MatchLSTM()
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.params['embedding_input_dim'] = 10000
      >>> model.params['embedding_output_dim'] = 100
      >>> model.params['embedding_trainable'] = True
      >>> model.params['fc_num_units'] = 200
      >>> model.params['lstm_num_units'] = 256
      >>> model.params['dropout_rate'] = 0.5
      >>> model.build()
docstr: Get default parameters.
docstr_tok: ["Get","default","parameters","."]
code: def get_default_params(cls):
      """Get default parameters."""
      params = super().get_default_params(with_embedding=True)
      params.add(
          Param(
              "lstm_num_units",
              256,
              hyper_space=hyper_spaces.quniform(
                  low=128, high=384, q=32
              ),
              desc="The hidden size in the LSTM layer.",
          )
      )
      params.add(
          Param(
              "fc_num_units",
              200,
              hyper_space=hyper_spaces.quniform(
                  low=100, high=300, q=20
              ),
              desc="The hidden size in the full connection layer.",
          )
      )
      params.add(
          Param(
              "dropout_rate",
              0.0,
              hyper_space=hyper_spaces.quniform(
                  low=0.0, high=0.9, q=0.01
              ),
              desc="The dropout rate.",
          )
      )
      return params
  
file_path: MatchZoo/matchzoo/contrib/models/match_lstm.py
imports: import keras
  import keras.backend
  import tensorflow
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param import Param
  from matchzoo.engine import hyper_spaces
label_desc: Use image node_id 1 for calling the MatchLSTM obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params